Real-Time Prediction of Neurally Mediated Syncope

Neurally mediated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue in our aging society, as its incidence increases with age. In this paper, we present a solution for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. Several parameters extracted from ECG and PPG, associated with reflectory mechanisms underlying NMS in previous publications, were combined in a single algorithm to detect impending syncope. The proposed algorithm was evaluated in a population of 43 subjects. The feature selection, distance metric selection, and optimal threshold were performed in a subset of 30 patients, while the remaining data from 13 patients were used to test the final solution. Additionally, a leave-one-out cross-validation scheme was also used to evaluate the performance of the proposed algorithm yielding the following results: sensitivity (SE)--95.2%; specificity (SP)--95.4%; positive predictive value (PPV)--90.9%; false-positive rate per hour (FPRh)-0.14 h(-1), and prediction time (aPTime)--116.4 s.